Your roadmap needs a product, not another AI experiment.
Product leaders carry two competing pressures: make the product more useful with AI, and protect the experience and reliability customers already pay for. The decision is not whether an LLM can perform a task. It is whether that capability belongs in the product.
Talk through your requirementsWhat earns a place on the roadmap?
Choose the customer job that matters, then establish whether AI improves it enough to justify product and engineering capacity.
- Custom work earns its place when
- A differentiated interaction belongs inside the product and needs its existing identity, state and domain behavior.
- Change course when
- The customer problem remains vague, or a simpler interaction or existing feature can meet it.
Ask to see the evidence.
- A user-outcome baseline and a credible way to measure adoption.
- An experience showing uncertainty, correction and manual fallback.
- Tenant tests and a usage-based operating cost estimate.
A decision about one feature.
Validate a complete user task and its hardest product or technical assumption before committing a broader roadmap.
The situation behind the request
Your team might have a backlog of AI concepts, customer pressure to add intelligence, or a prototype that impressed executives. Engineering now has questions about tenancy, cost, UX and failure states that the concept never answered.
Where the obvious solution falls short
The wrong first feature can consume roadmap capacity without creating adoption. Chat interfaces may be easy to demonstrate but hard to embed into a real customer job.
How we would approach the work
Start with a user outcome and the existing product context. Define how AI changes the workflow, what remains deterministic and how the feature will be measured. Build a validated slice with a realistic backend and controlled release.
What the engagement may include
- Product opportunity and feasibility framing.
- AI UX and interaction prototypes with failure/review states.
- Feature, API and data integration for existing platforms.
- Evaluation, observability and usage measurement.
The choices worth making explicitly
Do not commission a custom product if user demand is vague. A prototype can answer a question; a production build should follow evidence of value and technical feasibility.
What we would test before calling it ready
A buyer should be able to inspect user jobs, acceptance criteria, tenant boundaries, model evaluation and the rollout plan before signing off.
A sensible first step
Bring a specific product idea, customer task or roadmap constraint. The useful first engagement is the one that resolves a real product decision.
Bring us the
difficult part.
A product idea, a broken workflow, scattered company knowledge or an integration others keep working around. Tell us what needs to change and what has already been tried.
Discuss a project